In this experiment, we evaluate 6D object pose estimation using single RGB-D images as input. If the system can estimate object poses quickly and accurately from just a single image, it would significantly speed up manipulation tasks. We evaluate our pose initialization method (Fig. REF ) and the original PoseCNN [1]}. Both methods use the same network for segmentation and detection. The main difference is in rotation and translation estimation. PoseCNN [1]} is trained to directly regress to object distance and 3D rotation, while our method uses the auto-encoders and particle filter to estimate translation and rotation.
